Overview

Dataset statistics

Number of variables15
Number of observations11430
Missing cells0
Missing cells (%)0.0%
Duplicate rows690
Duplicate rows (%)6.0%
Total size in memory1.3 MiB
Average record size in memory120.0 B

Variable types

Categorical4
Numeric11

Alerts

suspicious_tld has constant value ""Constant
domain_age has constant value ""Constant
Dataset has 690 (6.0%) duplicate rowsDuplicates
hyphens is highly overall correlated with length and 1 other fieldsHigh correlation
length is highly overall correlated with hyphens and 1 other fieldsHigh correlation
special_char is highly overall correlated with hyphens and 1 other fieldsHigh correlation
status is uniformly distributedUniform
subdomain has 2058 (18.0%) zerosZeros
paths has 8207 (71.8%) zerosZeros
num_letter_ratio_path has 8276 (72.4%) zerosZeros
sensitive_keywords has 10060 (88.0%) zerosZeros
rand_numstring has 9736 (85.2%) zerosZeros
hyphens has 5992 (52.4%) zerosZeros

Reproduction

Analysis started2024-03-13 05:49:55.602464
Analysis finished2024-03-13 05:50:36.648219
Duration41.05 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

status
Categorical

UNIFORM 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
1
5715 
0
5715 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5715
50.0%
0 5715
50.0%

Length

2024-03-13T05:50:36.817257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T05:50:37.079965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 5715
50.0%
0 5715
50.0%

Most occurring characters

ValueCountFrequency (%)
1 5715
50.0%
0 5715
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5715
50.0%
0 5715
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5715
50.0%
0 5715
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5715
50.0%
0 5715
50.0%

length
Real number (ℝ)

HIGH CORRELATION 

Distinct323
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.120035
Minimum12
Maximum1641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-03-13T05:50:37.324480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile22
Q133
median47
Q371
95-th percentile131.55
Maximum1641
Range1629
Interquartile range (IQR)38

Descriptive statistics

Standard deviation55.29247
Coefficient of variation (CV)0.90465377
Kurtosis144.2501
Mean61.120035
Median Absolute Deviation (MAD)17
Skewness8.0870456
Sum698602
Variance3057.2573
MonotonicityNot monotonic
2024-03-13T05:50:37.637640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 251
 
2.2%
26 251
 
2.2%
29 250
 
2.2%
33 231
 
2.0%
27 230
 
2.0%
30 227
 
2.0%
34 224
 
2.0%
25 221
 
1.9%
35 219
 
1.9%
31 219
 
1.9%
Other values (313) 9107
79.7%
ValueCountFrequency (%)
12 1
 
< 0.1%
13 4
 
< 0.1%
14 2
 
< 0.1%
15 18
 
0.2%
16 20
 
0.2%
17 37
 
0.3%
18 56
0.5%
19 86
0.8%
20 84
0.7%
21 131
1.1%
ValueCountFrequency (%)
1641 1
< 0.1%
1386 2
< 0.1%
938 1
< 0.1%
907 1
< 0.1%
795 1
< 0.1%
648 1
< 0.1%
629 1
< 0.1%
611 1
< 0.1%
565 1
< 0.1%
557 1
< 0.1%

vowels
Real number (ℝ)

Distinct958
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41591002
Minimum0
Maximum1.1176471
Zeros24
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-03-13T05:50:37.957997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.33333333
median0.41666667
Q30.5
95-th percentile0.61904762
Maximum1.1176471
Range1.1176471
Interquartile range (IQR)0.16666667

Descriptive statistics

Standard deviation0.1277548
Coefficient of variation (CV)0.30716934
Kurtosis0.20838194
Mean0.41591002
Median Absolute Deviation (MAD)0.083333333
Skewness-0.050938555
Sum4753.8515
Variance0.01632129
MonotonicityNot monotonic
2024-03-13T05:50:38.260957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 509
 
4.5%
0.3333333333 451
 
3.9%
0.4 278
 
2.4%
0.4285714286 210
 
1.8%
0.25 178
 
1.6%
0.375 178
 
1.6%
0.4444444444 142
 
1.2%
0.2857142857 139
 
1.2%
0.3636363636 131
 
1.1%
0.3529411765 121
 
1.1%
Other values (948) 9093
79.6%
ValueCountFrequency (%)
0 24
0.2%
0.05555555556 1
 
< 0.1%
0.05882352941 2
 
< 0.1%
0.0625 5
 
< 0.1%
0.06666666667 14
0.1%
0.06976744186 1
 
< 0.1%
0.07142857143 9
 
0.1%
0.07692307692 13
0.1%
0.08333333333 20
0.2%
0.08695652174 2
 
< 0.1%
ValueCountFrequency (%)
1.117647059 1
< 0.1%
1 1
< 0.1%
0.9545454545 1
< 0.1%
0.9285714286 1
< 0.1%
0.9 1
< 0.1%
0.875 1
< 0.1%
0.8644067797 1
< 0.1%
0.8333333333 2
< 0.1%
0.8275862069 1
< 0.1%
0.8222222222 1
< 0.1%

subdomain
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0524934
Minimum0
Maximum13
Zeros2058
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-03-13T05:50:38.520781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.86109888
Coefficient of variation (CV)0.81815131
Kurtosis26.648208
Mean1.0524934
Median Absolute Deviation (MAD)0
Skewness3.2559518
Sum12030
Variance0.74149129
MonotonicityNot monotonic
2024-03-13T05:50:38.764449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 7803
68.3%
0 2058
 
18.0%
2 785
 
6.9%
3 640
 
5.6%
4 96
 
0.8%
5 15
 
0.1%
6 10
 
0.1%
11 9
 
0.1%
7 7
 
0.1%
8 3
 
< 0.1%
Other values (3) 4
 
< 0.1%
ValueCountFrequency (%)
0 2058
 
18.0%
1 7803
68.3%
2 785
 
6.9%
3 640
 
5.6%
4 96
 
0.8%
5 15
 
0.1%
6 10
 
0.1%
7 7
 
0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
12 2
 
< 0.1%
11 9
 
0.1%
9 1
 
< 0.1%
8 3
 
< 0.1%
7 7
 
0.1%
6 10
 
0.1%
5 15
 
0.1%
4 96
 
0.8%
3 640
5.6%

paths
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30288714
Minimum0
Maximum10
Zeros8207
Zeros (%)71.8%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-03-13T05:50:39.006535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.51823049
Coefficient of variation (CV)1.710969
Kurtosis15.325365
Mean0.30288714
Median Absolute Deviation (MAD)0
Skewness2.2116366
Sum3462
Variance0.26856284
MonotonicityNot monotonic
2024-03-13T05:50:39.232540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 8207
71.8%
1 3028
 
26.5%
2 168
 
1.5%
3 21
 
0.2%
6 2
 
< 0.1%
4 2
 
< 0.1%
10 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 8207
71.8%
1 3028
 
26.5%
2 168
 
1.5%
3 21
 
0.2%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
6 2
 
< 0.1%
5 1
 
< 0.1%
4 2
 
< 0.1%
3 21
 
0.2%
2 168
 
1.5%
1 3028
 
26.5%
0 8207
71.8%

https
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
6983 
1
4447 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6983
61.1%
1 4447
38.9%

Length

2024-03-13T05:50:39.502870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T05:50:39.748514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 6983
61.1%
1 4447
38.9%

Most occurring characters

ValueCountFrequency (%)
0 6983
61.1%
1 4447
38.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6983
61.1%
1 4447
38.9%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6983
61.1%
1 4447
38.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6983
61.1%
1 4447
38.9%

num_letter_ratio_path
Real number (ℝ)

ZEROS 

Distinct714
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9467031
Minimum0
Maximum113
Zeros8276
Zeros (%)72.4%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-03-13T05:50:39.998651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.72727273
95-th percentile11
Maximum113
Range113
Interquartile range (IQR)0.72727273

Descriptive statistics

Standard deviation6.0397609
Coefficient of variation (CV)3.1025588
Kurtosis64.409719
Mean1.9467031
Median Absolute Deviation (MAD)0
Skewness6.5943079
Sum22250.816
Variance36.478712
MonotonicityNot monotonic
2024-03-13T05:50:40.293940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8276
72.4%
0.8888888889 73
 
0.6%
2 71
 
0.6%
6 71
 
0.6%
4 64
 
0.6%
3 63
 
0.6%
0.7 61
 
0.5%
0.5454545455 61
 
0.5%
5 58
 
0.5%
9 58
 
0.5%
Other values (704) 2574
 
22.5%
ValueCountFrequency (%)
0 8276
72.4%
0.1333333333 3
 
< 0.1%
0.1428571429 1
 
< 0.1%
0.1851851852 1
 
< 0.1%
0.2 2
 
< 0.1%
0.2142857143 1
 
< 0.1%
0.2307692308 4
 
< 0.1%
0.25 2
 
< 0.1%
0.2564102564 1
 
< 0.1%
0.2962962963 1
 
< 0.1%
ValueCountFrequency (%)
113 1
< 0.1%
103 2
< 0.1%
89 1
< 0.1%
87 1
< 0.1%
79 1
< 0.1%
73 1
< 0.1%
72 2
< 0.1%
68 1
< 0.1%
66 2
< 0.1%
63 1
< 0.1%

num_letter_ratio_domain
Real number (ℝ)

Distinct324
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86154764
Minimum0
Maximum0.97674419
Zeros97
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-03-13T05:50:40.602938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.70833333
Q10.85714286
median0.88461538
Q30.90909091
95-th percentile0.94117647
Maximum0.97674419
Range0.97674419
Interquartile range (IQR)0.051948052

Descriptive statistics

Standard deviation0.10861561
Coefficient of variation (CV)0.12607035
Kurtosis33.408291
Mean0.86154764
Median Absolute Deviation (MAD)0.027472527
Skewness-5.0332161
Sum9847.4895
Variance0.011797351
MonotonicityNot monotonic
2024-03-13T05:50:40.925830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.875 871
 
7.6%
0.9 797
 
7.0%
0.8888888889 686
 
6.0%
0.8571428571 629
 
5.5%
0.8666666667 546
 
4.8%
0.8823529412 501
 
4.4%
0.9090909091 478
 
4.2%
0.9047619048 459
 
4.0%
0.8333333333 428
 
3.7%
0.8947368421 421
 
3.7%
Other values (314) 5614
49.1%
ValueCountFrequency (%)
0 97
0.8%
0.2 1
 
< 0.1%
0.2727272727 1
 
< 0.1%
0.2820512821 1
 
< 0.1%
0.2878787879 1
 
< 0.1%
0.3181818182 1
 
< 0.1%
0.3333333333 1
 
< 0.1%
0.3846153846 3
 
< 0.1%
0.3928571429 1
 
< 0.1%
0.4 2
 
< 0.1%
ValueCountFrequency (%)
0.976744186 4
< 0.1%
0.9761904762 1
 
< 0.1%
0.972972973 1
 
< 0.1%
0.9722222222 1
 
< 0.1%
0.9714285714 1
 
< 0.1%
0.9705882353 2
< 0.1%
0.9696969697 3
< 0.1%
0.96875 2
< 0.1%
0.9666666667 2
< 0.1%
0.9655172414 4
< 0.1%

special_char
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9787402
Minimum4
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-03-13T05:50:41.232148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q16
median8
Q312
95-th percentile19
Maximum145
Range141
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1619142
Coefficient of variation (CV)0.61750422
Kurtosis77.808072
Mean9.9787402
Median Absolute Deviation (MAD)2
Skewness6.2417502
Sum114057
Variance37.969187
MonotonicityNot monotonic
2024-03-13T05:50:41.554413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 2256
19.7%
7 1475
12.9%
8 1172
10.3%
9 1168
10.2%
10 943
8.3%
5 756
 
6.6%
11 692
 
6.1%
12 555
 
4.9%
13 417
 
3.6%
14 344
 
3.0%
Other values (53) 1652
14.5%
ValueCountFrequency (%)
4 90
 
0.8%
5 756
 
6.6%
6 2256
19.7%
7 1475
12.9%
8 1172
10.3%
9 1168
10.2%
10 943
8.3%
11 692
 
6.1%
12 555
 
4.9%
13 417
 
3.6%
ValueCountFrequency (%)
145 1
 
< 0.1%
113 1
 
< 0.1%
105 2
< 0.1%
99 3
< 0.1%
97 2
< 0.1%
90 2
< 0.1%
89 1
 
< 0.1%
84 1
 
< 0.1%
83 2
< 0.1%
75 2
< 0.1%

domain_length
Real number (ℝ)

Distinct83
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.100175
Minimum4
Maximum214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-03-13T05:50:41.859982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q115
median19
Q324
95-th percentile42
Maximum214
Range210
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.77833
Coefficient of variation (CV)0.51081708
Kurtosis69.784448
Mean21.100175
Median Absolute Deviation (MAD)4
Skewness5.158379
Sum241175
Variance116.17239
MonotonicityNot monotonic
2024-03-13T05:50:42.174990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 957
 
8.4%
15 754
 
6.6%
18 731
 
6.4%
17 726
 
6.4%
14 703
 
6.2%
19 588
 
5.1%
20 576
 
5.0%
21 562
 
4.9%
13 530
 
4.6%
22 492
 
4.3%
Other values (73) 4811
42.1%
ValueCountFrequency (%)
4 14
 
0.1%
5 16
 
0.1%
6 71
 
0.6%
7 71
 
0.6%
8 61
 
0.5%
9 101
 
0.9%
10 214
1.9%
11 318
2.8%
12 367
3.2%
13 530
4.6%
ValueCountFrequency (%)
214 2
< 0.1%
213 3
< 0.1%
212 1
 
< 0.1%
211 1
 
< 0.1%
179 1
 
< 0.1%
150 1
 
< 0.1%
122 1
 
< 0.1%
120 1
 
< 0.1%
95 1
 
< 0.1%
87 1
 
< 0.1%

sensitive_keywords
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14934383
Minimum0
Maximum6
Zeros10060
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-03-13T05:50:42.427020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44938687
Coefficient of variation (CV)3.0090755
Kurtosis20.20745
Mean0.14934383
Median Absolute Deviation (MAD)0
Skewness3.8232462
Sum1707
Variance0.20194856
MonotonicityNot monotonic
2024-03-13T05:50:42.652164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 10060
88.0%
1 1100
 
9.6%
2 218
 
1.9%
3 43
 
0.4%
4 6
 
0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
0 10060
88.0%
1 1100
 
9.6%
2 218
 
1.9%
3 43
 
0.4%
4 6
 
0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
6 3
 
< 0.1%
4 6
 
0.1%
3 43
 
0.4%
2 218
 
1.9%
1 1100
 
9.6%
0 10060
88.0%

suspicious_tld
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11430
100.0%

Length

2024-03-13T05:50:43.268742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T05:50:43.502996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11430
100.0%

Most occurring characters

ValueCountFrequency (%)
0 11430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11430
100.0%

rand_numstring
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25739283
Minimum0
Maximum37
Zeros9736
Zeros (%)85.2%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-03-13T05:50:43.715257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum37
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.94019014
Coefficient of variation (CV)3.6527442
Kurtosis287.08663
Mean0.25739283
Median Absolute Deviation (MAD)0
Skewness11.73084
Sum2942
Variance0.8839575
MonotonicityNot monotonic
2024-03-13T05:50:43.952995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 9736
85.2%
1 1107
 
9.7%
2 364
 
3.2%
4 83
 
0.7%
3 76
 
0.7%
5 20
 
0.2%
8 14
 
0.1%
6 6
 
0.1%
9 6
 
0.1%
10 3
 
< 0.1%
Other values (9) 15
 
0.1%
ValueCountFrequency (%)
0 9736
85.2%
1 1107
 
9.7%
2 364
 
3.2%
3 76
 
0.7%
4 83
 
0.7%
5 20
 
0.2%
6 6
 
0.1%
7 3
 
< 0.1%
8 14
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
37 1
 
< 0.1%
22 1
 
< 0.1%
19 1
 
< 0.1%
16 1
 
< 0.1%
15 2
 
< 0.1%
13 1
 
< 0.1%
12 2
 
< 0.1%
11 3
< 0.1%
10 3
< 0.1%
9 6
0.1%

hyphens
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.32021
Minimum0
Maximum43
Zeros5992
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-03-13T05:50:44.300329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum43
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3967712
Coefficient of variation (CV)1.815447
Kurtosis38.026025
Mean1.32021
Median Absolute Deviation (MAD)0
Skewness4.5045114
Sum15090
Variance5.7445123
MonotonicityNot monotonic
2024-03-13T05:50:44.715163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 5992
52.4%
1 2244
 
19.6%
2 1171
 
10.2%
3 773
 
6.8%
4 390
 
3.4%
5 261
 
2.3%
6 184
 
1.6%
7 119
 
1.0%
8 81
 
0.7%
9 64
 
0.6%
Other values (19) 151
 
1.3%
ValueCountFrequency (%)
0 5992
52.4%
1 2244
 
19.6%
2 1171
 
10.2%
3 773
 
6.8%
4 390
 
3.4%
5 261
 
2.3%
6 184
 
1.6%
7 119
 
1.0%
8 81
 
0.7%
9 64
 
0.6%
ValueCountFrequency (%)
43 1
 
< 0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
31 4
< 0.1%
30 1
 
< 0.1%
28 1
 
< 0.1%
27 3
< 0.1%
26 1
 
< 0.1%
20 3
< 0.1%
19 1
 
< 0.1%

domain_age
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11430
100.0%

Length

2024-03-13T05:50:45.111895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T05:50:45.545798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11430
100.0%

Most occurring characters

ValueCountFrequency (%)
0 11430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11430
100.0%

Interactions

2024-03-13T05:50:31.954300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:49:59.464037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:02.602665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:06.345481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:09.442988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:12.337856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:15.195517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:18.847339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:21.986727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:24.767539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:27.662948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:32.404757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:49:59.747461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:02.896102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:06.742724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:09.708612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:12.606567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:15.478038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:19.232006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:22.257886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:25.051730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:27.945835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:32.855233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:00.059601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:03.161361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:07.100801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:09.958011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:12.854064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:15.721165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:19.644999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:22.499359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:25.302837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:28.504291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:33.311684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:00.346082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:03.417566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:07.360240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:10.235709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:13.109473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:16.222472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:19.928475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:22.763689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:25.555168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:28.772007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:33.745262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:00.623137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:03.667873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:07.610424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:10.486325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:13.381369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:16.484263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:20.186183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:23.002545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:25.802899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:29.047290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:34.090641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:00.924033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:03.939057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:07.856215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:10.744184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:13.618508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:16.737262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:20.437608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:23.256195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:26.079463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:29.313348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:34.357298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:01.203458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:04.196810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:08.125075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:11.014292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:13.877003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:16.983521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:20.702399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:23.497241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:26.330906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:29.649423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:34.614172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:01.490139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:04.545737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:08.387248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:11.318770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:14.145153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:17.339999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:20.965141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:23.762096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:26.598204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:30.153173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:34.867706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:01.764648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:04.878510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:08.634636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:11.556343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:14.410766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:17.725579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:21.215376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:24.006081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:26.850260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:30.565141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:35.148293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:02.073205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:05.288742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:08.890937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:11.813359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:14.667056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:18.095694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:21.476188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:24.264639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:27.126507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:31.089541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:35.436281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:02.348565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:05.690143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:09.186295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:12.078927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:14.941078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:18.477545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:21.746698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:24.524073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:27.404885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T05:50:31.566617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-03-13T05:50:45.810042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
domain_lengthhttpshyphenslengthnum_letter_ratio_domainnum_letter_ratio_pathpathsrand_numstringsensitive_keywordsspecial_charstatussubdomainvowels
domain_length1.0000.0500.1210.2650.275-0.015-0.0500.1110.1970.1140.2760.4820.239
https0.0501.0000.016-0.054-0.068-0.024-0.154-0.024-0.028-0.0370.1140.055-0.108
hyphens0.1210.0161.0000.626-0.0660.3060.1190.2040.1860.7150.1290.0810.306
length0.265-0.0540.6261.0000.1280.4700.3820.4000.3230.9010.1520.1480.425
num_letter_ratio_domain0.275-0.068-0.0660.1281.0000.033-0.001-0.0570.057-0.0640.237-0.3950.246
num_letter_ratio_path-0.015-0.0240.3060.4700.0331.0000.1560.3430.1400.4380.0310.0120.113
paths-0.050-0.1540.1190.382-0.0010.1561.0000.1060.1260.4120.111-0.0420.025
rand_numstring0.111-0.0240.2040.400-0.0570.3430.1061.0000.1910.3420.1050.1030.139
sensitive_keywords0.197-0.0280.1860.3230.0570.1400.1260.1911.0000.3190.3370.1520.198
special_char0.114-0.0370.7150.901-0.0640.4380.4120.3420.3191.0000.1690.2120.340
status0.2760.1140.1290.1520.2370.0310.1110.1050.3370.1691.0000.007-0.101
subdomain0.4820.0550.0810.148-0.3950.012-0.0420.1030.1520.2120.0071.0000.020
vowels0.239-0.1080.3060.4250.2460.1130.0250.1390.1980.340-0.1010.0201.000

Missing values

2024-03-13T05:50:35.788750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T05:50:36.375137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

statuslengthvowelssubdomainpathshttpsnum_letter_ratio_pathnum_letter_ratio_domainspecial_chardomain_lengthsensitive_keywordssuspicious_tldrand_numstringhyphensdomain_age
01370.3636361100.0000000.89473771900000
10770.8275860001.5882350.95652272300000
201260.5172413010.8888890.900000195010030
31180.3000001000.0000000.81818251100000
41550.3636361000.0000000.866667101500020
50320.5000002000.0000000.83333382400010
61190.4000001000.0000000.83333351200000
70810.4054051001.0952380.92592682700100
81420.6363641000.0000000.94117663400000
911040.5918370018.0000000.9000001810001100
statuslengthvowelssubdomainpathshttpsnum_letter_ratio_pathnum_letter_ratio_domainspecial_chardomain_lengthsensitive_keywordssuspicious_tldrand_numstringhyphensdomain_age
1142011160.5454551016.6666670.9090912222000120
1142101170.47692310013.6000000.923077162610020
114221250.4285710000.0000000.94117651700000
114230850.3829792100.0000000.800000161500000
114241620.4411761000.0000000.866667131500060
114251450.3571431000.0000000.88235371700000
114260840.48888912014.5000000.888889151800000
1142711050.5208331012.6000000.875000171600170
114281380.3913041000.0000000.93333363000000
1142904770.5318182000.0000000.0000009914000180

Duplicate rows

Most frequently occurring

statuslengthvowelssubdomainpathshttpsnum_letter_ratio_pathnum_letter_ratio_domainspecial_chardomain_lengthsensitive_keywordssuspicious_tldrand_numstringhyphensdomain_age# duplicates
3791250.2666671010.00.8750006160000037
4011260.2500001010.00.8823536170000033
3621240.2857141010.00.8666676150000031
4081260.3333331010.00.8823536170000028
4461280.2941181010.00.8947376190000028
4271270.3125001010.00.8888896180000026
3361230.2142861010.00.8571436140000024
4191270.2352941010.00.8888896180000023
3221220.2307691010.00.8461546130000021
3781250.2666671000.00.8823536170000021